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Sushmita Paul

Bio: Sushmita Paul is an academic researcher from Indian Institute of Technology, Jodhpur. The author has contributed to research in topics: Cluster analysis & Rough set. The author has an hindex of 12, co-authored 57 publications receiving 575 citations. Previous affiliations of Sushmita Paul include University of Erlangen-Nuremberg & Indian Statistical Institute.


Papers
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Book ChapterDOI
05 Dec 2017
TL;DR: An existing robust mutual information based Maximum-Relevance Maximum-Significance algorithm has been used and is found to generate more robust integrated networks of miRNA-mRNA in ovarian cancer.
Abstract: Ovarian cancer is a fatal gynecologic cancer. Altered expression of biomarkers leads to this deadly cancer. Therefore, understanding the underlying biological mechanisms may help in developing a robust diagnostic as well as a prognostic tool. It has been demonstrated in various studies the pathways associated with ovarian cancer have dysregulated miRNA as well as mRNA expression. Identification of miRNA-mRNA regulatory modules may help in understanding the mechanism of altered ovarian cancer pathways. In this regard, an existing robust mutual information based Maximum-Relevance Maximum-Significance algorithm has been used for identification of miRNA-mRNA regulatory modules in ovarian cancer. A set of miRNA-mRNA modules are identified first than their association with ovarian cancer are studied exhaustively. The effectiveness of the proposed approach is compared with existing methods. The proposed approach is found to generate more robust integrated networks of miRNA-mRNA in ovarian cancer.

1 citations

Book ChapterDOI
01 Jan 2014
TL;DR: A computational method to identify disease genes, judiciously integrating the information of gene expression profiles and shortest path analysis of protein-protein interaction networks is reported, indicating that it may become a useful tool for identifying disease genes.
Abstract: One of the most important and challenging problems in functional genomics is how to select the disease genes. In this chapter, a computational method is reported to identify disease genes, judiciously integrating the information of gene expression profiles and shortest path analysis of protein-protein interaction networks. While the gene expression profiles have been used to select differentially expressed genes as disease genes using mutual information-based maximum relevance-maximum significance framework, the functional protein association network has been used to study the mechanism of diseases. Extensive experimental study on colorectal cancer establishes the fact that the genes identified by the integrated method have more colorectal cancer genes than the genes identified from the gene expression profiles alone. All these results indicate that the integrated method is quite promising and may become a useful tool for identifying disease genes.
Book ChapterDOI
30 Jun 2015
TL;DR: A new rough hypercuboid based supervised similarity measure is proposed that is integrated with the supervised attribute clustering to find groups of miRNAs whose coherent expression can classify samples.
Abstract: The microRNAs are small, endogenous non-coding RNAs found in plants and animals, which suppresses the expression of genes post-transcriptionally. It is suggested by various genome-wide studies that a substantial fraction of miRNA genes is likely to form clusters. The coherent expression of the miRNA clusters can then be used to classify samples according to the clinical outcome. In this background, a new rough hypercuboid based supervised similarity measure is proposed that is integrated with the supervised attribute clustering to find groups of miRNAs whose coherent expression can classify samples. The proposed method directly incorporates the information of sample categories into the miRNA clustering process, generating a supervised clustering algorithm for miRNAs. The effectiveness of the rough hypercuboid based algorithm, along with a comparison with other related algorithms, is demonstrated on three miRNA microarray expression data sets using the \(B.632+\) bootstrap error rate of support vector machine. The association of the miRNA clusters to various biological pathways are also shown by doing pathway enrichment analysis.
Proceedings ArticleDOI
15 Aug 2022
TL;DR: A novel pipeline is designed that uses a feature selection step prior to association tests to identify a crisp set of SNPs that are significantly associated with the trait under consideration and outperforms the other methods.
Abstract: Genome-wide Association Studies (GWA studies) are performed to identify genetic variants like Single Nucleotide Polymorphisms (SNPs) significantly associated with phenotype in case-control or cohort study designs. GWA studies are based on the fundamental assumption that the most statistically significant variants have a more decisive influence on the phenotype. Thus, most GWA studies use statistical approaches to identify the variants lying below a significant threshold. However, the conventional statistical techniques fail to identify significant variants for complex traits by simply thresholding since the traits are driven by both genetic and environmental factors. Therefore, it is critical to design approaches, which can capture SNPs that significantly affect the complex traits. To address this, several machine learning algorithms are being designed. However, all such techniques face the problem of a low sample to feature ratio creating redundancy and uncertainty in GWA studies. Therefore, a novel pipeline is designed that uses a feature selection step prior to association tests to identify a crisp set of SNPs that are significantly associated with the trait under consideration. The proposed pipeline combines a Rough set-based relevance technique with a machine learning-based association test called Support Vector Regression to identify cholesterol-associated SNPs. The pipeline reduces the number of SNPs to the most relevant SNPs and decreases the time required for association testing. A comparative performance analysis of the proposed approach over other existing approaches is illustrated on the pennCATH cohort dataset through R2 statistics and biological analyses. The proposed pipeline outperforms the other methods. SNP and gene enrichment studies reveal various genes, pathways and biological processes significantly related to cholesterol with the SNPs obtained from the proposed pipeline and establish the fact that the performance of the proposed rough-set-based feature selection method is significantly better.

Cited by
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01 Jan 2002

9,314 citations

Journal ArticleDOI
TL;DR: The hallmarks of cancer conceptualization is a heuristic tool for distilling the vast complexity of cancer phenotypes and genotypes into a provisional set of underlying principles as mentioned in this paper , which are used to understand mechanisms of cancer development and malignant progression, and apply that knowledge to cancer medicine.
Abstract: The hallmarks of cancer conceptualization is a heuristic tool for distilling the vast complexity of cancer phenotypes and genotypes into a provisional set of underlying principles. As knowledge of cancer mechanisms has progressed, other facets of the disease have emerged as potential refinements. Herein, the prospect is raised that phenotypic plasticity and disrupted differentiation is a discrete hallmark capability, and that nonmutational epigenetic reprogramming and polymorphic microbiomes both constitute distinctive enabling characteristics that facilitate the acquisition of hallmark capabilities. Additionally, senescent cells, of varying origins, may be added to the roster of functionally important cell types in the tumor microenvironment. SIGNIFICANCE: Cancer is daunting in the breadth and scope of its diversity, spanning genetics, cell and tissue biology, pathology, and response to therapy. Ever more powerful experimental and computational tools and technologies are providing an avalanche of "big data" about the myriad manifestations of the diseases that cancer encompasses. The integrative concept embodied in the hallmarks of cancer is helping to distill this complexity into an increasingly logical science, and the provisional new dimensions presented in this perspective may add value to that endeavor, to more fully understand mechanisms of cancer development and malignant progression, and apply that knowledge to cancer medicine.

1,838 citations

19 Nov 2012

1,653 citations

Journal ArticleDOI
TL;DR: The prospect is raised that phenotypic plasticity and disrupted differentiation is a discrete hallmark capability, and that nonmutational epigenetic reprogramming and polymorphic microbiomes both constitute distinctive enabling characteristics that facilitate the acquisition of hallmark capabilities.
Abstract: The hallmarks of cancer conceptualization is a heuristic tool for distilling the vast complexity of cancer phenotypes and genotypes into a provisional set of underlying principles. As knowledge of cancer mechanisms has progressed, other facets of the disease have emerged as potential refinements. Herein, the prospect is raised that phenotypic plasticity and disrupted differentiation is a discrete hallmark capability, and that nonmutational epigenetic reprogramming and polymorphic microbiomes both constitute distinctive enabling characteristics that facilitate the acquisition of hallmark capabilities. Additionally, senescent cells, of varying origins, may be added to the roster of functionally important cell types in the tumor microenvironment. SIGNIFICANCE: Cancer is daunting in the breadth and scope of its diversity, spanning genetics, cell and tissue biology, pathology, and response to therapy. Ever more powerful experimental and computational tools and technologies are providing an avalanche of "big data" about the myriad manifestations of the diseases that cancer encompasses. The integrative concept embodied in the hallmarks of cancer is helping to distill this complexity into an increasingly logical science, and the provisional new dimensions presented in this perspective may add value to that endeavor, to more fully understand mechanisms of cancer development and malignant progression, and apply that knowledge to cancer medicine.

1,480 citations

Journal ArticleDOI

1,073 citations